14.1.3.5 Ranking

Sima, C.[Chao], Dougherty, E.R.[Edward R.],
Optimal convex error estimators for classification,
PR(39), No. 9, September 2006, pp. 1763-1780.
Elsevier DOI
WWW Link. 0606
Bootstrap, Cross-validation, Error estimation; Feature-set ranking, Optimal estimation, Resubstitution, BibRef

Wei, H.L.[Hua-Liang], Billings, S.A.,
Feature Subset Selection and Ranking for Data Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 162-166.
IEEE DOI 0701
Forward Orthogonal Search. Select features 1 at a time. BibRef

Liang, J.N.[Jian-Ning], Yang, S.[Su], Winstanley, A.[Adam],
Invariant optimal feature selection: A distance discriminant and feature ranking based solution,
PR(41), No. 5, May 2008, pp. 1429-1439.
Elsevier DOI 0711
Optimal feature selection, Distance discriminant, Feature ranking BibRef

Yang, S.[Su], Liang, J.N.[Jian-Ning], Wang, Y.Y.[Yuan-Yuan], Winstanley, A.[Adam],
Feature Selection Based on Run Covering,
PSIVT06(208-217).
Springer DOI 0612
BibRef

Hong, Y.[Yi], Kwong, S.[Sam], Chang, Y.C.[Yu-Chou], Ren, Q.S.[Qing-Sheng],
Consensus unsupervised feature ranking from multiple views,
PRL(29), No. 5, 1 April 2008, pp. 595-602.
Elsevier DOI 0802
Clustering, Feature ranking ensembles, Unsupervised feature selection BibRef

Uematsu, K., Lee, Y.,
Statistical Optimality in Multipartite Ranking and Ordinal Regression,
PAMI(37), No. 5, May 2015, pp. 1080-1094.
IEEE DOI 1504
Measurement BibRef

Bellal, F.[Fazia], Elghazel, H.[Haytham], Aussem, A.[Alex],
A semi-supervised feature ranking method with ensemble learning,
PRL(33), No. 10, 15 July 2012, pp. 1426-1433.
Elsevier DOI 1205
Semi-supervised learning, Feature selection, Ensemble learning BibRef

Hernandez-Leal, P.[Pablo], Carrasco-Ochoa, J.A.[J. Ariel], Martínez-Trinidad, J.F.[José Francisco], Olvera-Lopez, J.A.[J. Arturo],
InstanceRank based on borders for instance selection,
PR(46), No. 1, January 2013, pp. 365-375.
Elsevier DOI 1209
Instance selection, Instance ranking, Border instances, Supervised classification BibRef

Olvera-López, J.A.[J. Arturo], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[J. Ariel],
Mixed Data Object Selection Based on Clustering and Border Objects,
CIARP07(674-683).
Springer DOI 0711
Instance selection. BibRef

Hernandez-Rodriguez, S.[Selene], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[J. Ariel],
On the selection of base prototypes for LAESA and TLAESA classifiers,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Jiang, Y.G.[Yu-Gang], Wang, J.[Jun], Xue, X., Chang, S.F.[Shih-Fu],
Query-Adaptive Image Search With Hash Codes,
MultMed(15), No. 2, 2013, pp. 442-453.
IEEE DOI 1302
BibRef

Jiang, Y.G.[Yu-Gang], Wang, J.[Jun], Chang, S.F.[Shih-Fu],
Lost in binarization: query-adaptive ranking for similar image search with compact codes,
ICMR11(16).
DOI Link 1301
BibRef
And: A2, A1, A3:
Label diagnosis through self tuning for web image search,
CVPR09(1390-1397).
IEEE DOI 0906
Are the initial label good? BibRef

Cánovas-García, F.[Fulgencio], Alonso-Sarría, F.[Francisco],
Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery,
RS(7), No. 4, 2015, pp. 4651-4677.
DOI Link 1505
BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Feature selection for multi-label classification using multivariate mutual information,
PRL(34), No. 3, 1 February 2013, pp. 349-357.
Elsevier DOI 1301
Multi-label feature selection, Multivariate feature selection; Multivariate mutual information, Label dependency BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
SCLS: Multi-label feature selection based on scalable criterion for large label set,
PR(66), No. 1, 2017, pp. 342-352.
Elsevier DOI 1704
Machine learning BibRef

Lim, H.K.[Hyun-Ki], Kim, D.W.[Dae-Won],
Convex optimization approach for multi-label feature selection based on mutual information,
ICPR16(1512-1517)
IEEE DOI 1705
Convex functions, Entropy, Linear programming, Mutual information, Optimization, Redundancy, Time, complexity BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation,
IEICE(E99-D), No. 5, May 2016, pp. 1396-1399.
WWW Link. 1605
BibRef

Lim, H.K.[Hyun-Ki],
Low-rank learning for feature selection in multi-label classification,
PRL(172), 2023, pp. 106-112.
Elsevier DOI 2309
Multi-label classification, Feature selection, Low-rank learning BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Optimization approach for feature selection in multi-label classification,
PRL(89), No. 1, 2017, pp. 25-30.
Elsevier DOI 1704
Multi-label feature selection BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Fast multi-label feature selection based on information-theoretic feature ranking,
PR(48), No. 9, 2015, pp. 2761-2771.
Elsevier DOI 1506
Multi-label feature selection BibRef

Senawi, A.[Azlyna], Wei, H.L.[Hua-Liang], Billings, S.A.[Stephen A.],
A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking,
PR(67), No. 1, 2017, pp. 47-61.
Elsevier DOI 1704
Dimensionality reduction BibRef

Ji, Z., Cui, B., Li, H., Jiang, Y., Xiang, T., Hospedales, T.M.[Timothy M.], Fu, Y.,
Deep Ranking for Image Zero-Shot Multi-Label Classification,
IP(29), 2020, pp. 6549-6560.
IEEE DOI 2007
Testing, Training, Predictive models, Semantics, Correlation, Visualization, Training data, Multi-label classification, transductive learning BibRef

Chen, Z.M.[Zhao-Min], Cui, Q.[Quan], Wei, X.S.[Xiu-Shen], Jin, X.[Xin], Guo, Y.[Yanwen],
Disentangling, Embedding and Ranking Label Cues for Multi-Label Image Recognition,
MultMed(23), 2021, pp. 1827-1840.
IEEE DOI 2107
Correlation, Image recognition, Streaming media, Recurrent neural networks, Task analysis, Computational modeling, ranking BibRef

Viola, R.[Rémi], Gautheron, L.[Léo], Habrard, A.[Amaury], Sebban, M.[Marc],
MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data,
PRL(161), 2022, pp. 161-167.
Elsevier DOI 2209
Imbalanced learning, Tree-based ranking, Average precision, Interpretability BibRef

Fu, Z.[Zheren], Mao, Z.D.[Zhen-Dong], Yan, C.G.[Cheng-Gang], Liu, A.A.[An-An], Xie, H.T.[Hong-Tao], Zhang, Y.D.[Yong-Dong],
Self-Supervised Synthesis Ranking for Deep Metric Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4736-4750.
IEEE DOI 2207
Measurement, Semantics, Transforms, Training, Task analysis, Coordinate measuring machines, Manifolds, Deep metric learning, generative model BibRef

Geng, X.[Xin], Zheng, R.Y.[Ren-Yi], Lv, J.Q.[Jia-Qi], Zhang, Y.[Yu],
Multilabel Ranking with Inconsistent Rankers,
PAMI(44), No. 9, September 2022, pp. 5211-5224.
IEEE DOI 2208
Training, Predictive models, Adaptation models, Task analysis, Machine learning, Machine learning algorithms, Encoding BibRef

Geng, X.[Xin], Luo, L.[Longrun],
Multilabel Ranking with Inconsistent Rankers,
CVPR14(3742-3747)
IEEE DOI 1409
BibRef

Helm, H.S.[Hayden S.], Basu, A.[Amitabh], Athreya, A.[Avanti], Park, Y.[Youngser], Vogelstein, J.T.[Joshua T.], Priebe, C.E.[Carey E.], Winding, M.[Michael], Zlatic, M.[Marta], Cardona, A.[Albert], Bourke, P.[Patrick], Larson, J.[Jonathan], Abdin, M.[Marah], Choudhury, P.[Piali], Yang, W.W.[Wei-Wei], White, C.W.[Christopher W.],
Distance-based positive and unlabeled learning for ranking,
PR(134), 2023, pp. 109085.
Elsevier DOI 2212
Positive-and-unlabeled learning, ranking, network analysis BibRef

Fu, Y.Q.[Yu-Qian], Xie, Y.[Yu], Fu, Y.W.[Yan-Wei], Jiang, Y.G.[Yu-Gang],
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning,
CVPR23(24575-24584)
IEEE DOI 2309
BibRef

Fu, Y.Q.[Yu-Qian], Fu, Y.W.[Yan-Wei], Chen, J.J.[Jing-Jing], Jiang, Y.G.[Yu-Gang],
Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data,
IP(31), 2022, pp. 7078-7090.
IEEE DOI 2212
Feature extraction, Task analysis, Training, Data models, Data mining, Benchmark testing, Visualization, contrastive learning BibRef

Xu, C.M.[Cheng-Ming], Fu, Y.W.[Yan-Wei], Liu, C.[Chen], Wang, C.J.[Cheng-Jie], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue], Zhang, L.[Li], Xue, X.Y.[Xiang-Yang],
Learning Dynamic Alignment via Meta-Filter for Few-Shot Learning,
CVPR21(5178-5187)
IEEE DOI 2111
Visualization, Adaptation models, Semantics, Benchmark testing, Ordinary differential equations, Information filters BibRef

Li, P.[Pan], Gong, S.G.[Shao-Gang], Wang, C.J.[Cheng-Jie], Fu, Y.W.[Yan-Wei],
Ranking Distance Calibration for Cross-Domain Few-Shot Learning,
CVPR22(9089-9098)
IEEE DOI 2210
Training, Image retrieval, Encoding, Calibration, Task analysis, Representation learning BibRef

Lei, Y.M.[Yi-Ming], Li, Z.L.[Zi-Long], Li, Y.Y.[Yang-Yang], Zhang, J.P.[Jun-Ping], Shan, H.M.[Hong-Ming],
CORE: Learning consistent ordinal representations with convex optimization for image ordinal estimation,
PR(156), 2024, pp. 110748.
Elsevier DOI 2408
Predictions with ordered labels (facial age, disease progression, aesthetics). Ordinal regression, Image ordinal estimation, Convex optimization, Dual decomposition BibRef

Sun, X.X.[Xiao-Xiao], Hou, Y.Z.[Yun-Zhong], Deng, W.J.[Wei-Jian], Li, H.D.[Hong-Dong], Zheng, L.[Liang],
Ranking Models in Unlabeled New Environments,
ICCV21(11741-11751)
IEEE DOI 2203
Measurement, Codes, Annotations, Computational modeling, Search problems, Task analysis, Image and video retrieval, Datasets and evaluation BibRef

Li, Y.D.[Yan-Dong], Jia, X.[Xuhui], Sang, R.X.[Ruo-Xin], Zhu, Y.K.[Yu-Kun], Green, B.[Bradley], Wang, L.Q.[Li-Qiang], Gong, B.Q.[Bo-Qing],
Ranking Neural Checkpoints,
CVPR21(2662-2672)
IEEE DOI 2111
To use in transfer learning. Deep learning, Training, Network topology, Transfer learning, Benchmark testing, Feature extraction, Topology BibRef

Vargas-Ruíz, L.[Lauro], Franco-Arcega, A.[Anilu], Alonso-Lavernia, M.[María_de_los_Ángeles],
A Novel Criterion to Obtain the Best Feature Subset from Filter Ranking Methods,
MCPR18(12-22).
Springer DOI 1807
BibRef

Li, Y., Song, Y., Luo, J.,
Improving Pairwise Ranking for Multi-label Image Classification,
CVPR17(1837-1845)
IEEE DOI 1711
Adaptation models, Fasteners, Neural networks, Visualization BibRef

Yao, Y., Xin, X., Guo, P.,
A rank minimization-based late fusion method for multi-label image annotation,
ICPR16(847-852)
IEEE DOI 1705
Matrix decomposition, Minimization, Optimization, Predictive models, Sparse matrices, Training BibRef

Kanehira, A., Harada, T.,
Multi-label Ranking from Positive and Unlabeled Data,
CVPR16(5138-5146)
IEEE DOI 1612
BibRef

Cruz, R.[Ricardo], Fernandes, K.[Kelwin], Pinto Costa, J.F.[Joaquim F.], Ortiz, M.P.[María Pérez], Cardoso, J.S.[Jaime S.],
Ordinal Class Imbalance with Ranking,
IbPRIA17(3-12).
Springer DOI 1706
BibRef

Nogueira, S.[Sarah], Sechidis, K.[Konstantinos], Brown, G.[Gavin],
On the Use of Spearman's Rho to Measure the Stability of Feature Rankings,
IbPRIA17(381-391).
Springer DOI 1706
stability to training data perturbations. BibRef

Chen, L.[Lin], Zhang, Q.A.[Qi-Ang], Li, B.X.[Bao-Xin],
Predicting Multiple Attributes via Relative Multi-task Learning,
CVPR14(1027-1034)
IEEE DOI 1409
learn ranking functions describing the relative strength of attributes. BibRef

Shankar, S.[Sukrit], Lasenby, J.[Joan], Cipolla, R.[Roberto],
Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes,
ICCV13(361-368)
IEEE DOI 1403
Ranking attributes for classification. Optimization, Ranking, Semantic Descriptions BibRef

Shi, Z.Y.[Zhi-Yuan], Siva, P.[Parthipan], Xiang, T.[Tony],
Transfer Learning by Ranking for Weakly Supervised Object Annotation,
BMVC12(78).
DOI Link 1301
BibRef

Diamantini, C.[Claudia], Gemelli, A.[Alberto], Potena, D.[Domenico],
Feature Ranking Based on Decision Border,
ICPR10(609-612).
IEEE DOI 1008
BibRef

Parakhin, M.[Mikhail], Haluptzok, P.[Patrick],
Finding the Most Probable Ranking of Objects with Probabilistic Pairwise Preferences,
ICDAR09(616-620).
IEEE DOI 0907
Ranking when pairwise ranking is inconsistent (not transitive). apply to handwriting. BibRef

Bucak, S.S.[Serhat S.], Mallapragada, P.K.[Pavan Kumar], Jin, R.[Rong], Jain, A.K.[Anil K.],
Efficient multi-label ranking for multi-class learning: Application to object recognition,
ICCV09(2098-2105).
IEEE DOI 0909
Not just binary classification. Order the many possible classes. BibRef

Merler, M.[Michele], Yan, R.[Rong], Smith, J.R.[John R.],
Imbalanced RankBoost for efficiently ranking large-scale image/video collections,
CVPR09(2607-2614).
IEEE DOI 0906
BibRef

Li, Y.[Yun], Lu, B.L.[Bao-Liang], Wu, Z.F.[Zhong-Fu],
A Hybrid Method of Unsupervised Feature Selection Based on Ranking,
ICPR06(II: 687-690).
IEEE DOI 0609
BibRef

Zhu, X.Q.[Xing-Quan], Wu, X.D.[Xin-Dong],
Scalable Representative Instance Selection and Ranking,
ICPR06(III: 352-355).
IEEE DOI 0609
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Probabilistic Latent Semantic Analysis, pLSA. .